RWDisEnh+: Enhancing disease-enhancer association prediction through multiplex-heterogeneous networks - Duc-Hau Le (PLoS ONE, 2026)
Enhancers are critical regulatory DNA elements that, when dysregulated, can contribute to disease pathogenesis by altering gene expression. Although millions of enhancers have been identified through large-scale genomic projects, their associations with human diseases remain largely uncharacterized, emphasizing the need for robust computational approaches. In our previous work, we developed RWDisEnh, a network-based method that integrates a shared gene–based enhancer network with a disease similarity network within a heterogeneous framework to predict disease–enhancer associations. In this study, we present RWDisEnh+ , an enhanced version of RWDisEnh that incorporates a sequence-based enhancer similarity network into a multiplex-heterogeneous network to improve prediction performance. Using an extended random walk with restart (RWR) algorithm, RWDisEnh+ allows information to propagate across disease and enhancer layers, leveraging both gene-based and sequence-based similarity features to rank candidate enhancers for each disease. Comprehensive evaluation using 3-fold cross-validation demonstrated that RWDisEnh+ achieves an average AUC of 0.874, outperforming RWDisEnh’s AUC of 0.819. Moreover, RWDisEnh+ identifies a larger number of evidence-supported disease–enhancer associations across top-k rankings, including 10 enhancers linked to seven diseases such as asthma, rheumatoid arthritis, and type 2 diabetes. GWAS validation and pathway enrichment analyses further reveal that these predicted associations are enriched in immune, inflammatory, and metabolic pathways, highlighting their biological relevance. Overall, RWDisEnh+ provides a stable and effective framework for predicting novel disease–enhancer associations, offering new insights into enhancer-mediated gene regulation and the genetic architecture of complex diseases.